An Improved PSO Based on Diversity of Particle Symmetrical Distribution
SUN Yue-Hong1,2,WEI Jian-Xiang3,XIA De-Shen1
1.School of Computer Science and Technology,Nanjing University of Science and Technology,Nanjing 210094 2.School of Mathematical Sciences,Nanjing Normal University,Nanjing 210046 3.Department of Information Science,Nanjing College for Population Programme Management,Nanjing 210042
Abstract：Particle swarm optimization (PSO) is easy to fall into the local optimum as the diversity of population gets worse and worse during the evolution. To overcome the shortcoming, an improved PSO based on the diversity of particle symmetrical distribution (sdPSO) is developed. Over the research of the spatial distribution of particles, it can be found that the convergence probability to the global optimum solution is greatly improved with more symmetrical particle distribution surrounding the optimum solution of particles. A diversity population function is proposed and an adjustment algorithm for the diversity is introduced into the basic PSO. The spatial distribution of particles varies between asymmetry and symmetry repeatedly while the population diversity is adjusted continually, which make the improved algorithm search in a wider range. The simulation results show that the improved sdPSO algorithm achieves better convergence precision than the basic PSO by the experiment of benchmark functions.
孙越泓，魏建香，夏德深. 一种基于粒子对称分布多样性的PSO算法[J]. 模式识别与人工智能, 2010, 23(2): 137-143.
SUN Yue-Hong,WEI Jian-Xiang,XIA De-Shen. An Improved PSO Based on Diversity of Particle Symmetrical Distribution. , 2010, 23(2): 137-143.
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2010, Vol. 23 
